Monitoring catastrophic failure event in milling process using acoustic emission

This research focused on the monitoring catastrophic failure event in milling process using acoustic emission. Acoustic Emission (AE) is a naturally occurring phenomenon whereby external stimuli, such as mechanical loading, generate sources of elastic waves. AE occurs when a small surface displaceme...

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Main Author:	Haslan, Mohd Yong
Format:	Undergraduates Project Papers
Language:	English
Published:	2010
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/1792/ http://umpir.ump.edu.my/id/eprint/1792/1/Haslan_Mohd_Yong_%28_CD_4992_%29.pdf

id	ump-1792
recordtype	eprints
spelling	ump-17922015-03-03T07:52:53Z http://umpir.ump.edu.my/id/eprint/1792/ Monitoring catastrophic failure event in milling process using acoustic emission Haslan, Mohd Yong TA Engineering (General). Civil engineering (General) This research focused on the monitoring catastrophic failure event in milling process using acoustic emission. Acoustic Emission (AE) is a naturally occurring phenomenon whereby external stimuli, such as mechanical loading, generate sources of elastic waves. AE occurs when a small surface displacement of a material is produced. This occurs due to stress waves generated when there is a rapid release of energy in a material, or on its surface. The wave generated by the AE source will be used to stimulate and capture AE in inspection, quality control, system feedback, process monitoring and others. In this thesis, the acoustic emission will be studied by carrying out experiments (milling) on the work piece and determine the material properties also dynamics of machines using acoustic emission detector. There are three cutting speeds and five conditions of depth of cut chosen for the experiments. The depths of cut and cutting speed are generated in the experiments and an acoustic emission sensor detects the acoustic emission signals and transfers it to the acoustic emission software. Then, the software generates the signals into RMS signal. Data taken from the software are plotted into a graph of RMS versus depth of cut. The experiment continued to determine the properties of materials using Inverted Microscopes (IM). Pictures of anomalies of the cutting tool, work piece and chipping have been taken from inverted microscope for observation and compared with acoustic emission graph (RMS). After that, the result of graph and figure are detail explained. Then, conclusion and recommendation has been made. Finally, a stable combination of machining parameter (spindle speed and depth of cut) is proposed and applied during milling process in order to reduce the failures in the milling process. 2010-12 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1792/1/Haslan_Mohd_Yong_%28_CD_4992_%29.pdf Haslan, Mohd Yong (2010) Monitoring catastrophic failure event in milling process using acoustic emission. Faculty of Mechanical Engineering, Universiti Malaysia Pahang.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Haslan, Mohd Yong Monitoring catastrophic failure event in milling process using acoustic emission
description	This research focused on the monitoring catastrophic failure event in milling process using acoustic emission. Acoustic Emission (AE) is a naturally occurring phenomenon whereby external stimuli, such as mechanical loading, generate sources of elastic waves. AE occurs when a small surface displacement of a material is produced. This occurs due to stress waves generated when there is a rapid release of energy in a material, or on its surface. The wave generated by the AE source will be used to stimulate and capture AE in inspection, quality control, system feedback, process monitoring and others. In this thesis, the acoustic emission will be studied by carrying out experiments (milling) on the work piece and determine the material properties also dynamics of machines using acoustic emission detector. There are three cutting speeds and five conditions of depth of cut chosen for the experiments. The depths of cut and cutting speed are generated in the experiments and an acoustic emission sensor detects the acoustic emission signals and transfers it to the acoustic emission software. Then, the software generates the signals into RMS signal. Data taken from the software are plotted into a graph of RMS versus depth of cut. The experiment continued to determine the properties of materials using Inverted Microscopes (IM). Pictures of anomalies of the cutting tool, work piece and chipping have been taken from inverted microscope for observation and compared with acoustic emission graph (RMS). After that, the result of graph and figure are detail explained. Then, conclusion and recommendation has been made. Finally, a stable combination of machining parameter (spindle speed and depth of cut) is proposed and applied during milling process in order to reduce the failures in the milling process.
format	Undergraduates Project Papers
author	Haslan, Mohd Yong
author_facet	Haslan, Mohd Yong
author_sort	Haslan, Mohd Yong
title	Monitoring catastrophic failure event in milling process using acoustic emission
title_short	Monitoring catastrophic failure event in milling process using acoustic emission
title_full	Monitoring catastrophic failure event in milling process using acoustic emission
title_fullStr	Monitoring catastrophic failure event in milling process using acoustic emission
title_full_unstemmed	Monitoring catastrophic failure event in milling process using acoustic emission
title_sort	monitoring catastrophic failure event in milling process using acoustic emission
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1792/ http://umpir.ump.edu.my/id/eprint/1792/1/Haslan_Mohd_Yong_%28_CD_4992_%29.pdf
first_indexed	2023-09-18T21:55:02Z
last_indexed	2023-09-18T21:55:02Z
_version_	1777414022486818816

Monitoring catastrophic failure event in milling process using acoustic emission

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